# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=invalid-name
# pylint: disable=missing-docstring
"""EfficientNet models for Keras.

Reference:
  - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
      https://arxiv.org/abs/1905.11946) (ICML 2019)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

import copy
import math

from keras import backend
from keras.applications import imagenet_utils
from keras.engine import training
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils
from tensorflow.python.util.tf_export import keras_export


BASE_WEIGHTS_PATH = 'https://storage.googleapis.com/keras-applications/'

WEIGHTS_HASHES = {
    'b0': ('902e53a9f72be733fc0bcb005b3ebbac',
           '50bc09e76180e00e4465e1a485ddc09d'),
    'b1': ('1d254153d4ab51201f1646940f018540',
           '74c4e6b3e1f6a1eea24c589628592432'),
    'b2': ('b15cce36ff4dcbd00b6dd88e7857a6ad',
           '111f8e2ac8aa800a7a99e3239f7bfb39'),
    'b3': ('ffd1fdc53d0ce67064dc6a9c7960ede0',
           'af6d107764bb5b1abb91932881670226'),
    'b4': ('18c95ad55216b8f92d7e70b3a046e2fc',
           'ebc24e6d6c33eaebbd558eafbeedf1ba'),
    'b5': ('ace28f2a6363774853a83a0b21b9421a',
           '38879255a25d3c92d5e44e04ae6cec6f'),
    'b6': ('165f6e37dce68623721b423839de8be5',
           '9ecce42647a20130c1f39a5d4cb75743'),
    'b7': ('8c03f828fec3ef71311cd463b6759d99',
           'cbcfe4450ddf6f3ad90b1b398090fe4a'),
}

DEFAULT_BLOCKS_ARGS = [{
    'kernel_size': 3,
    'repeats': 1,
    'filters_in': 32,
    'filters_out': 16,
    'expand_ratio': 1,
    'id_skip': True,
    'strides': 1,
    'se_ratio': 0.25
}, {
    'kernel_size': 3,
    'repeats': 2,
    'filters_in': 16,
    'filters_out': 24,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 2,
    'se_ratio': 0.25
}, {
    'kernel_size': 5,
    'repeats': 2,
    'filters_in': 24,
    'filters_out': 40,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 2,
    'se_ratio': 0.25
}, {
    'kernel_size': 3,
    'repeats': 3,
    'filters_in': 40,
    'filters_out': 80,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 2,
    'se_ratio': 0.25
}, {
    'kernel_size': 5,
    'repeats': 3,
    'filters_in': 80,
    'filters_out': 112,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 1,
    'se_ratio': 0.25
}, {
    'kernel_size': 5,
    'repeats': 4,
    'filters_in': 112,
    'filters_out': 192,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 2,
    'se_ratio': 0.25
}, {
    'kernel_size': 3,
    'repeats': 1,
    'filters_in': 192,
    'filters_out': 320,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 1,
    'se_ratio': 0.25
}]

CONV_KERNEL_INITIALIZER = {
    'class_name': 'VarianceScaling',
    'config': {
        'scale': 2.0,
        'mode': 'fan_out',
        'distribution': 'truncated_normal'
    }
}

DENSE_KERNEL_INITIALIZER = {
    'class_name': 'VarianceScaling',
    'config': {
        'scale': 1. / 3.,
        'mode': 'fan_out',
        'distribution': 'uniform'
    }
}

layers = VersionAwareLayers()

BASE_DOCSTRING = """Instantiates the {name} architecture.

  Reference:
  - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
      https://arxiv.org/abs/1905.11946) (ICML 2019)

  Optionally loads weights pre-trained on ImageNet.
  Note that the data format convention used by the model is
  the one specified in your Keras config at `~/.keras/keras.json`.
  If you have never configured it, it defaults to `"channels_last"`.

  Args:
    include_top: Whether to include the fully-connected
        layer at the top of the network. Defaults to True.
    weights: One of `None` (random initialization),
          'imagenet' (pre-training on ImageNet),
          or the path to the weights file to be loaded. Defaults to 'imagenet'.
    input_tensor: Optional Keras tensor
        (i.e. output of `layers.Input()`)
        to use as image input for the model.
    input_shape: Optional shape tuple, only to be specified
        if `include_top` is False.
        It should have exactly 3 inputs channels.
    pooling: Optional pooling mode for feature extraction
        when `include_top` is `False`. Defaults to None.
        - `None` means that the output of the model will be
            the 4D tensor output of the
            last convolutional layer.
        - `avg` means that global average pooling
            will be applied to the output of the
            last convolutional layer, and thus
            the output of the model will be a 2D tensor.
        - `max` means that global max pooling will
            be applied.
    classes: Optional number of classes to classify images
        into, only to be specified if `include_top` is True, and
        if no `weights` argument is specified. Defaults to 1000 (number of
        ImageNet classes).
    classifier_activation: A `str` or callable. The activation function to use
        on the "top" layer. Ignored unless `include_top=True`. Set
        `classifier_activation=None` to return the logits of the "top" layer.
        Defaults to 'softmax'.

  Returns:
    A `keras.Model` instance.
"""


def EfficientNet(
    width_coefficient,
    depth_coefficient,
    default_size,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    activation='swish',
    blocks_args='default',
    model_name='efficientnet',
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the EfficientNet architecture using given scaling coefficients.

  Reference:
  - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
      https://arxiv.org/abs/1905.11946) (ICML 2019)

  Optionally loads weights pre-trained on ImageNet.
  Note that the data format convention used by the model is
  the one specified in your Keras config at `~/.keras/keras.json`.

  Args:
    width_coefficient: float, scaling coefficient for network width.
    depth_coefficient: float, scaling coefficient for network depth.
    default_size: integer, default input image size.
    dropout_rate: float, dropout rate before final classifier layer.
    drop_connect_rate: float, dropout rate at skip connections.
    depth_divisor: integer, a unit of network width.
    activation: activation function.
    blocks_args: list of dicts, parameters to construct block modules.
    model_name: string, model name.
    include_top: whether to include the fully-connected
        layer at the top of the network.
    weights: one of `None` (random initialization),
          'imagenet' (pre-training on ImageNet),
          or the path to the weights file to be loaded.
    input_tensor: optional Keras tensor
        (i.e. output of `layers.Input()`)
        to use as image input for the model.
    input_shape: optional shape tuple, only to be specified
        if `include_top` is False.
        It should have exactly 3 inputs channels.
    pooling: optional pooling mode for feature extraction
        when `include_top` is `False`.
        - `None` means that the output of the model will be
            the 4D tensor output of the
            last convolutional layer.
        - `avg` means that global average pooling
            will be applied to the output of the
            last convolutional layer, and thus
            the output of the model will be a 2D tensor.
        - `max` means that global max pooling will
            be applied.
    classes: optional number of classes to classify images
        into, only to be specified if `include_top` is True, and
        if no `weights` argument is specified.
    classifier_activation: A `str` or callable. The activation function to use
        on the "top" layer. Ignored unless `include_top=True`. Set
        `classifier_activation=None` to return the logits of the "top" layer.

  Returns:
    A `keras.Model` instance.

  Raises:
    ValueError: in case of invalid argument for `weights`,
      or invalid input shape.
    ValueError: if `classifier_activation` is not `softmax` or `None` when
      using a pretrained top layer.
  """
  if blocks_args == 'default':
    blocks_args = DEFAULT_BLOCKS_ARGS

  if not (weights in {'imagenet', None} or tf.io.gfile.exists(weights)):
    raise ValueError('The `weights` argument should be either '
                     '`None` (random initialization), `imagenet` '
                     '(pre-training on ImageNet), '
                     'or the path to the weights file to be loaded.')

  if weights == 'imagenet' and include_top and classes != 1000:
    raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
                     ' as true, `classes` should be 1000')

  # Determine proper input shape
  input_shape = imagenet_utils.obtain_input_shape(
      input_shape,
      default_size=default_size,
      min_size=32,
      data_format=backend.image_data_format(),
      require_flatten=include_top,
      weights=weights)

  if input_tensor is None:
    img_input = layers.Input(shape=input_shape)
  else:
    if not backend.is_keras_tensor(input_tensor):
      img_input = layers.Input(tensor=input_tensor, shape=input_shape)
    else:
      img_input = input_tensor

  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  def round_filters(filters, divisor=depth_divisor):
    """Round number of filters based on depth multiplier."""
    filters *= width_coefficient
    new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_filters < 0.9 * filters:
      new_filters += divisor
    return int(new_filters)

  def round_repeats(repeats):
    """Round number of repeats based on depth multiplier."""
    return int(math.ceil(depth_coefficient * repeats))

  # Build stem
  x = img_input
  x = layers.Rescaling(1. / 255.)(x)
  x = layers.Normalization(axis=bn_axis)(x)

  x = layers.ZeroPadding2D(
      padding=imagenet_utils.correct_pad(x, 3),
      name='stem_conv_pad')(x)
  x = layers.Conv2D(
      round_filters(32),
      3,
      strides=2,
      padding='valid',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name='stem_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name='stem_bn')(x)
  x = layers.Activation(activation, name='stem_activation')(x)

  # Build blocks
  blocks_args = copy.deepcopy(blocks_args)

  b = 0
  blocks = float(sum(round_repeats(args['repeats']) for args in blocks_args))
  for (i, args) in enumerate(blocks_args):
    assert args['repeats'] > 0
    # Update block input and output filters based on depth multiplier.
    args['filters_in'] = round_filters(args['filters_in'])
    args['filters_out'] = round_filters(args['filters_out'])

    for j in range(round_repeats(args.pop('repeats'))):
      # The first block needs to take care of stride and filter size increase.
      if j > 0:
        args['strides'] = 1
        args['filters_in'] = args['filters_out']
      x = block(
          x,
          activation,
          drop_connect_rate * b / blocks,
          name='block{}{}_'.format(i + 1, chr(j + 97)),
          **args)
      b += 1

  # Build top
  x = layers.Conv2D(
      round_filters(1280),
      1,
      padding='same',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name='top_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name='top_bn')(x)
  x = layers.Activation(activation, name='top_activation')(x)
  if include_top:
    x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
    if dropout_rate > 0:
      x = layers.Dropout(dropout_rate, name='top_dropout')(x)
    imagenet_utils.validate_activation(classifier_activation, weights)
    x = layers.Dense(
        classes,
        activation=classifier_activation,
        kernel_initializer=DENSE_KERNEL_INITIALIZER,
        name='predictions')(x)
  else:
    if pooling == 'avg':
      x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
    elif pooling == 'max':
      x = layers.GlobalMaxPooling2D(name='max_pool')(x)

  # Ensure that the model takes into account
  # any potential predecessors of `input_tensor`.
  if input_tensor is not None:
    inputs = layer_utils.get_source_inputs(input_tensor)
  else:
    inputs = img_input

  # Create model.
  model = training.Model(inputs, x, name=model_name)

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      file_suffix = '.h5'
      file_hash = WEIGHTS_HASHES[model_name[-2:]][0]
    else:
      file_suffix = '_notop.h5'
      file_hash = WEIGHTS_HASHES[model_name[-2:]][1]
    file_name = model_name + file_suffix
    weights_path = data_utils.get_file(
        file_name,
        BASE_WEIGHTS_PATH + file_name,
        cache_subdir='models',
        file_hash=file_hash)
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)
  return model


def block(inputs,
          activation='swish',
          drop_rate=0.,
          name='',
          filters_in=32,
          filters_out=16,
          kernel_size=3,
          strides=1,
          expand_ratio=1,
          se_ratio=0.,
          id_skip=True):
  """An inverted residual block.

  Args:
      inputs: input tensor.
      activation: activation function.
      drop_rate: float between 0 and 1, fraction of the input units to drop.
      name: string, block label.
      filters_in: integer, the number of input filters.
      filters_out: integer, the number of output filters.
      kernel_size: integer, the dimension of the convolution window.
      strides: integer, the stride of the convolution.
      expand_ratio: integer, scaling coefficient for the input filters.
      se_ratio: float between 0 and 1, fraction to squeeze the input filters.
      id_skip: boolean.

  Returns:
      output tensor for the block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  # Expansion phase
  filters = filters_in * expand_ratio
  if expand_ratio != 1:
    x = layers.Conv2D(
        filters,
        1,
        padding='same',
        use_bias=False,
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'expand_conv')(
            inputs)
    x = layers.BatchNormalization(axis=bn_axis, name=name + 'expand_bn')(x)
    x = layers.Activation(activation, name=name + 'expand_activation')(x)
  else:
    x = inputs

  # Depthwise Convolution
  if strides == 2:
    x = layers.ZeroPadding2D(
        padding=imagenet_utils.correct_pad(x, kernel_size),
        name=name + 'dwconv_pad')(x)
    conv_pad = 'valid'
  else:
    conv_pad = 'same'
  x = layers.DepthwiseConv2D(
      kernel_size,
      strides=strides,
      padding=conv_pad,
      use_bias=False,
      depthwise_initializer=CONV_KERNEL_INITIALIZER,
      name=name + 'dwconv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name=name + 'bn')(x)
  x = layers.Activation(activation, name=name + 'activation')(x)

  # Squeeze and Excitation phase
  if 0 < se_ratio <= 1:
    filters_se = max(1, int(filters_in * se_ratio))
    se = layers.GlobalAveragePooling2D(name=name + 'se_squeeze')(x)
    if bn_axis == 1:
      se_shape = (filters, 1, 1)
    else:
      se_shape = (1, 1, filters)
    se = layers.Reshape(se_shape, name=name + 'se_reshape')(se)
    se = layers.Conv2D(
        filters_se,
        1,
        padding='same',
        activation=activation,
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'se_reduce')(
            se)
    se = layers.Conv2D(
        filters,
        1,
        padding='same',
        activation='sigmoid',
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'se_expand')(se)
    x = layers.multiply([x, se], name=name + 'se_excite')

  # Output phase
  x = layers.Conv2D(
      filters_out,
      1,
      padding='same',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name=name + 'project_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name=name + 'project_bn')(x)
  if id_skip and strides == 1 and filters_in == filters_out:
    if drop_rate > 0:
      x = layers.Dropout(
          drop_rate, noise_shape=(None, 1, 1, 1), name=name + 'drop')(x)
    x = layers.add([x, inputs], name=name + 'add')
  return x


@keras_export('keras.applications.efficientnet.EfficientNetB0',
              'keras.applications.EfficientNetB0')
def EfficientNetB0(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.0,
      1.0,
      224,
      0.2,
      model_name='efficientnetb0',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB1',
              'keras.applications.EfficientNetB1')
def EfficientNetB1(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.0,
      1.1,
      240,
      0.2,
      model_name='efficientnetb1',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB2',
              'keras.applications.EfficientNetB2')
def EfficientNetB2(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.1,
      1.2,
      260,
      0.3,
      model_name='efficientnetb2',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB3',
              'keras.applications.EfficientNetB3')
def EfficientNetB3(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.2,
      1.4,
      300,
      0.3,
      model_name='efficientnetb3',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB4',
              'keras.applications.EfficientNetB4')
def EfficientNetB4(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.4,
      1.8,
      380,
      0.4,
      model_name='efficientnetb4',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB5',
              'keras.applications.EfficientNetB5')
def EfficientNetB5(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.6,
      2.2,
      456,
      0.4,
      model_name='efficientnetb5',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB6',
              'keras.applications.EfficientNetB6')
def EfficientNetB6(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.8,
      2.6,
      528,
      0.5,
      model_name='efficientnetb6',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB7',
              'keras.applications.EfficientNetB7')
def EfficientNetB7(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      2.0,
      3.1,
      600,
      0.5,
      model_name='efficientnetb7',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


EfficientNetB0.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB0')
EfficientNetB1.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB1')
EfficientNetB2.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB2')
EfficientNetB3.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB3')
EfficientNetB4.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB4')
EfficientNetB5.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB5')
EfficientNetB6.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB6')
EfficientNetB7.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB7')


@keras_export('keras.applications.efficientnet.preprocess_input')
def preprocess_input(x, data_format=None):  # pylint: disable=unused-argument
  return x


@keras_export('keras.applications.efficientnet.decode_predictions')
def decode_predictions(preds, top=5):
  return imagenet_utils.decode_predictions(preds, top=top)


decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
